We conduct all the experiments on 8\(\times \) Nvidia V100 GPUs. We train a MaskFormer model on COCO Stuff dataset with ResNet-101 as default backbone. An AdamW optimizer with the initial learning rate of 1e-4, weight decay of 1e-4 and a backbone multiplier of 0.1, and a poly learning rate policy with a power of 0.9 are used. The batch size is set to 32 for each GPU, and the total training iteration is 60K. If not specified, the MaskFormer model only trained on seen classes and ignore the unseen classes, and we use 100 mask proposals for both training and testing. For all other settings and hyper-parameters, we keep the official setting of [1]} without changes.
